基于车载激光点云的路面坑槽检测方法

Pavement Pothole Detection Method Based on Vehicle-Borne Laser Point Clouds

针对基于车载激光点云的坑槽检测受道路横、纵坡度影响而导致误检和漏检等问题,提出了一种联合粗糙度与负偏态分布的路面坑槽检测方法。首先利用垂直度分割路面点云。然后,通过M估计样本一致性(MSAC)拟合局部基准平面,并计算点云的粗糙度;以粗糙度较小的区域作为潜在坑槽区域,并利用密度聚类和连续度实现潜在坑槽的单体化。最后,根据坑槽与邻域路面点云的粗糙度统计特征,结合负偏态分布实现坑槽区域的精确筛选,并提取坑槽的三维几何特征。采用开源数据和实测数据进行实验验证与分析。实验结果表明:实测数据路面中,坑槽检测的召回率达到89.2%,准确率达到76.7%;坑槽几何特征的提取结果与人工实地测量结果的最大相对偏差为9.4%,可为大规模的路面损坏检测提供有力支撑。

Objective Road safety is crucial for public well-being and economic prosperity.Accurate and comprehensive pothole inspection is essential to identify potential safety hazards early and take prompt maintenance measures to ensure public safety.Traditional manual inspection has drawbacks including limited staff safety,slow and expensive processes,etc.Hence,efficient and automated technologies and methods are urgently needed for pavement pothole inspection.Intelligent inspection research focusing on safety enhancements includes vibration anomaly detection,two-dimensional(2D)image processing,and three-dimensional(3D)stereo detection.However,vibration anomaly detection methods may suffer from leakage,while 2D image-based detection methods are susceptible to environmental factors like light,shadows,and water,leading to inaccurate results.Additionally,in 3D stereo detection methods,line structured light scanning technology is limited to single-lane scanning,while 3D reconstruction methods are more demanding in terms of images and algorithms,showing lower robustness.Existing pothole detection methods from vehicle-borne laser point cloud rely on fitting local line or surface models and using height differences to identify pothole,but accuracy is compromised due to the complexity and slope of the pavement.Inaccurate local models and relative distances are significant factors contributing to the incorrect extraction or omission.To address these challenges,we propose a novel method for detecting pavement potholes from vehicle-borne laser point clouds.The goal is to assist road maintenance departments in inspecting and maintaining pavements more effectively,ultimately enhancing the efficiency of pavement damage extraction.Methods Addressing the challenges associated with pothole detection using vehicle-borne laser point clouds,which can be influenced by road transverse and longitudinal slopes leading to misdetection and omission,in this paper we propose a novel pothole detection method based on roughness and negative skewed distribution.The method involves three main steps:pavement point cloud segmentation,pothole preliminary separation,and statistical quantitative judgement.To begin with,the cloth simulation filter(CSF)algorithm is used to obtain ground point clouds,followed by the segmentation of pavement point clouds from the complex road scenes through verticality and hierarchical clustering.Subsequently,a local plane model is fitted using the M-estimated sample consistency(MSAC)method to obtain the relative directed distance(i.e.,roughness),enabling the localization of potential potholes.Densitybased spatial clustering of applications with noise(DBSCAN)and point cloud continuity are then utilized for the singularization and denoising of potential potholes.Next,a neighborhood expansion process is conducted for potential monolithic potholes,and their identification is accurately determined based on the statistical laws of roughness distribution and the skewness coefficients.Geometric features such as depth,projected area,and repair size are computed considering the independence and regional connectivity of the potholes.Finally,experiments are conducted using both open source data and measured data to validate the effectiveness and accuracy of the proposed method.Results and Discussions Based on the continuity and flatness of the pavement,as well as the vertical characteristics of road curbs and their separation as pavement boundaries,this study firstly acquires the accurate pavement point clouds(Fig.14).The proposed method can accurately detect potholes in multiple lanes and different shapes in both open source data(Figs.15 and 16)and measured data(Figs.17 and 18),which proves the effectiveness of the proposed method.Field inspections of the measured data scene reveal impressive results for pothole detection using the proposed method,with a recall rate of 89.2%and an accuracy rate of 76.7%.Notably,both indicators outperform similar methods by over 10%(Table 2).Additionally,the maximum relative deviation of potholes 3D geometric features between the proposed method and manual field measurement is 9.4%(Table 3 and Fig.19),further highlighting the applicability and robustness of the proposed method.The experimental results demonstrate the applicability and robustness of the proposed method,which can avoid the inaccuracy of the relative distance due to local grids(Fig.3)and further improve the judgement of potholes by statistical features.Conclusions In this study,a novel method for pavement pothole detection that integrates roughness and negative skewed distribution is proposed.Firstly,the pavement point cloud is extracted from the intricate road environment using the CSF method,along with verticality and hierarchical clustering.Then,MSAC is used to fit the planes in order to obtain accurate local planes and relative distances.For the noise issue,DBSCAN and point cloud continuity are used for denoising and singularization of potential potholes.To achieve accurate judgement of potholes,the potential potholes along with their neighboring pavement point cloud are taken as a whole,and the statistical features of roughness are used for quantitative judgement of potholes.Finally,3D geometric features such as depth,projected area,length and width of potholes are automatically extracted from the point cloud.Experimental results demonstrate the effectiveness of the proposed method in detecting potholes within large-scale complex road scenes.In the measured data,the recall rate and accuracy rate of pothole detection reach 89.2%and 76.7%,respectively.The maximum relative deviation between extracted 3D geometric features and manually measured field results is only 9.4%.Overall,the proposed method offers a valuable technical reference for extracting pavement damage information,enabling accurate detection of road damage and precise evaluation of road conditions.